{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6284,
     "status": "ok",
     "timestamp": 1583461808336,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "dkIJ5orxp7Nc",
    "outputId": "a6c38ee3-e339-46eb-cf3e-b29a17b5ac5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'nCov2019_analysis' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "! git clone https://github.com/jianxu305/nCov2019_analysis/\n",
    "#! git pull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YiCtZv8aqASf"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "os.chdir('/content/nCov2019_analysis/src')\n",
    "\n",
    "sys.path.append('/content/nCov2019_analysis/src')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LYgkJCsgqP14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import utils   # some convenient functions\n",
    "import matplotlib.font_manager as mfm\n",
    "import datetime\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6831,
     "status": "ok",
     "timestamp": 1583461808968,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "yth1KlwfslkQ",
    "outputId": "33befe25-df25-4894-f64e-f7b2d06b9ae1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last update:  2020-03-06 09:20:02.598000\n",
      "Data date range:  2020-01-24 to 2020-03-06\n",
      "Number of rows in raw data:  64790\n"
     ]
    }
   ],
   "source": [
    "dxy_raw = utils.load_chinese_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mWkO9PYiswpO"
   },
   "outputs": [],
   "source": [
    "dxy_frm = utils.aggDaily(dxy_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UEKLvWIheOqI"
   },
   "outputs": [],
   "source": [
    "jhs_raw = utils.load_jhs_raw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ytXSdb87Of91"
   },
   "outputs": [],
   "source": [
    "jhs_frm = utils.jhs_daily(jhs_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 326
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 46095,
     "status": "ok",
     "timestamp": 1583461848296,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "dTWZri5fOnBb",
    "outputId": "c95c6c3e-21e4-4d8a-cc8b-f10c4139363e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province/state</th>\n",
       "      <th>country/region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>cum_confirmed</th>\n",
       "      <th>cum_dead</th>\n",
       "      <th>cum_cured</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>update_time</th>\n",
       "      <th>update_date</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_dead</th>\n",
       "      <th>new_cured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>Santa Rosa County, FL</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-05T20:03:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.7690</td>\n",
       "      <td>-86.9824</td>\n",
       "      <td>2020-03-05 20:03:03</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>Snohomish County, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-05T20:23:07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0330</td>\n",
       "      <td>-121.8339</td>\n",
       "      <td>2020-03-05 20:23:07</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>Westchester County, NY</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-05T21:33:03</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.1220</td>\n",
       "      <td>-73.7949</td>\n",
       "      <td>2020-03-05 21:33:03</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>Williamson County, TN</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-05T23:33:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.9179</td>\n",
       "      <td>-86.8622</td>\n",
       "      <td>2020-03-05 23:33:03</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td></td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2020-03-05T23:53:02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>54.0000</td>\n",
       "      <td>2020-03-05 23:53:02</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              province/state        country/region  ... new_dead  new_cured\n",
       "2263   Santa Rosa County, FL                    US  ...      0.0        0.0\n",
       "2264    Snohomish County, WA                    US  ...      0.0        0.0\n",
       "2265  Westchester County, NY                    US  ...      0.0        0.0\n",
       "2266   Williamson County, TN                    US  ...      0.0        0.0\n",
       "2267                          United Arab Emirates  ...      0.0        0.0\n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jhs_frm.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XjiQu56eMgvQ"
   },
   "source": [
    "## 1. Sanity Check\n",
    "### 1.1 Take a Brief Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 47211,
     "status": "ok",
     "timestamp": 1583461849455,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "z3cm-ThnMkVW",
    "outputId": "002dfe83-9250-4f9a-b4ef-9bfb6539606d"
   },
   "outputs": [
    {
     "data": {
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MyxwTv99H4jXVk9BeZhbQj/C30JXQg9JrJPQeVWxfpxD+Xj1eJ+0T5u1EaGPxbZz/CqFx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\ngg==\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = utils.tsplot_conf_dead_cured(jhs_frm[jhs_frm['country/region'] == 'South Korea'], \n",
    "                                   title='Confirmed, Dead, Cured within South Korea', figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tMaiFAL8oyl2"
   },
   "source": [
    "### 1.2 Compare with Ding Xiang Yuan Chinese Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lu49nXo6ouzt"
   },
   "outputs": [],
   "source": [
    "jhs_china = jhs_frm[jhs_frm['country/region'] == 'Mainland China'].groupby(['update_date'])[['cum_confirmed', 'cum_dead']].agg('sum')\n",
    "jhs_china = jhs_china.add_suffix('_JHS').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TzuUpE4RQBYr"
   },
   "outputs": [],
   "source": [
    "dxy_china = dxy_frm.groupby(['update_date'])[['cum_confirmed', 'cum_dead']].agg('sum')\n",
    "dxy_china = dxy_china.add_suffix('_DXY').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t6KcGgsJQmPO"
   },
   "outputs": [],
   "source": [
    "comp = pd.merge(jhs_china, dxy_china, on='update_date').set_index('update_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 47285,
     "status": "ok",
     "timestamp": 1583461849693,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "beJmI-mzXHnG",
    "outputId": "af8735db-aec7-4926-bad8-27519e4f631d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c310c8550>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pISKLRGSn9Tvcyisi8r6IJIrIRhHpVOA8I6z8O0VkRIH0ziKyyTrmffGVdStV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\n8TSRMcYYBwNjjDE4GBhjjMHBwBhjDPA/9I9sQE9OC5QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp.plot(y=['cum_confirmed_DXY', 'cum_confirmed_JHS'], marker='*', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 47759,
     "status": "ok",
     "timestamp": 1583461850203,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "cWZHFBpGXrAi",
    "outputId": "fd1553aa-801f-4323-9e34-a6124612e3ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c31224e80>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FqL3HuXtqJDGHkwlrUosP/9FFu3DURdOgr1RFsOwNa0jmvpVQ+xK4ewHUbXPO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\nHrgj9AEKMd4BKEToAxQi9AEKEfoAhRwPtkM6CMXB2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp.plot(y=['cum_dead_DXY', 'cum_dead_JHS'], marker='*', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2sFu8Jp5NIG2"
   },
   "source": [
    "## 2. Mortality Rate vs Confirmed Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 50547,
     "status": "ok",
     "timestamp": 1583461853024,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "egit-TifdN9I",
    "outputId": "8cf0c7cd-e4fc-4fc4-e335-98dc636baccf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: chart_studio in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from chart_studio) (4.4.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from chart_studio) (2.21.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.12.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2019.11.28)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (1.24.3)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install chart_studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4OefTkNUvQ4t"
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import chart_studio\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qYwdMHItYi4B"
   },
   "outputs": [],
   "source": [
    "api_key  = ''  # use you own key\n",
    "user_name = ''  # use your own user name\n",
    "chart_studio.tools.set_credentials_file(username=user_name, api_key=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0UyNzX7WdOi4"
   },
   "outputs": [],
   "source": [
    "# most international data only has country info, so do not drill down to finer level now\n",
    "country_frm = jhs_frm.groupby(['country/region', 'update_date']).agg('sum')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qa48ptEoBIZp"
   },
   "source": [
    "### Remove Some Data Problem\n",
    "Japan and US has some dates with negative new_confirm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 50784,
     "status": "ok",
     "timestamp": 1583461853420,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "jqkIWY7iA_YU",
    "outputId": "ce232528-8b33-47b4-bfc5-9bba0bd404bc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>cum_confirmed</th>\n",
       "      <th>cum_dead</th>\n",
       "      <th>cum_cured</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_dead</th>\n",
       "      <th>new_cured</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country/region</th>\n",
       "      <th>update_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Japan</th>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            cum_confirmed  cum_dead  ...  new_dead  new_cured\n",
       "country/region update_date                           ...                     \n",
       "Japan          2020-01-23             1.0       0.0  ...       0.0        0.0\n",
       "               2020-02-07            25.0       0.0  ...       0.0        0.0\n",
       "\n",
       "[2 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_frm[country_frm['new_confirmed'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7oPc6od1f15U"
   },
   "outputs": [],
   "source": [
    "country_frm['new_confirmed'] = np.maximum(0, country_frm['new_confirmed'])   # Japan 1/23, 2/7, US 2/24 have new confirmed < 0, looks like data error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lab92pyKBh7D"
   },
   "source": [
    "### Add Mortality Rate, Moving Average, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dAruDomBdcnV"
   },
   "outputs": [],
   "source": [
    "country_frm['mortality_rate'] = country_frm['cum_dead'] / country_frm['cum_confirmed']\n",
    "country_frm = utils.add_moving_average(country_frm, ['country/region'], win_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fen9OlCfhp2C"
   },
   "outputs": [],
   "source": [
    "# The following columns are mainly for plot rendering\n",
    "country_frm['new_confirmed_logMA'] = np.log10(country_frm['new_confirmed_MA'] + 2)\n",
    "color_countries = ['Mainland China', 'Japan', 'South Korea', 'Iran', 'Italy', 'Germany', \n",
    "                   'US', 'France', 'Spain', 'Diamond Princess', 'Thailandl', 'Taiwan', 'Hong Kong',\n",
    "                   'Singapore', 'UK', 'Iraq']\n",
    "country_frm['color'] = np.where(country_frm.index.get_level_values('country/region').isin(color_countries), \n",
    "                                country_frm.index.get_level_values('country/region'), \n",
    "                                'Other')\n",
    "country_ex_china = country_frm[country_frm.index.get_level_values('country/region') != 'Mainland China']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 684,
     "status": "ok",
     "timestamp": 1583457844937,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "-MJWr8js6QL9",
    "outputId": "462332bf-0547-45cc-9584-74100dd48f34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BrFq7cP2e8u3ow/z9cMztx9uV8kzLahHx0/qNJcBpwNsiYn/K4BWXZB/D2mfmIxGxN3Bq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\np5hWugj4U0RcULa9uorzRM3zHwBHlc9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      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = utils.cross_sectional_bar(country_ex_china.reset_index(), '2020-03-03', 'cum_confirmed', 'country/region', 10,\n",
    "                                title='Top 10 contry/region with the most confirmed cases outside Mainland China')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jk6hdHhOYXj4"
   },
   "source": [
    "#### Flashback to 2020-02-26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1178,
     "status": "ok",
     "timestamp": 1583462600580,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "XDNY6GZ9crP3",
    "outputId": "4370f7b9-96be-4f6d-85c2-fa14bce4415d"
   },
   "outputs": [
    {
     "data": {
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     "metadata": {
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   ],
   "source": [
    "date = datetime.date(2020, 2, 26)\n",
    "frm = country_ex_china.loc[(slice(None), date), :].reset_index()\n",
    "#frm = frm[frm['cum_confirmed'] >= 5]  # only show countries with more than 5 confirmed cases\n",
    "frm = frm[frm['color'] != 'Other']\n",
    "fig = px.scatter(frm,\n",
    "                 x='cum_confirmed', \n",
    "                 y='mortality_rate',  \n",
    "                 size='new_confirmed_logMA', \n",
    "                 size_max=frm['new_confirmed_logMA'].max() * 8,\n",
    "                 color='color',\n",
    "                 text='country/region',\n",
    "                 #color_continuous_scale=px.colors.sequential.Jet,\n",
    "                 hover_data=['country/region', 'cum_dead', 'cum_confirmed', 'new_confirmed', 'new_dead'],\n",
    "                 title='Mortality Rate vs Confirmed by Country/Region as of ' + str(date))\n",
    "fig.update_layout(xaxis_type=\"log\", xaxis_title='Confirmed Count', yaxis_title='Mortality Rate')\n",
    "fig.layout.yaxis.tickformat = ',.1%'  # set display to % format\n",
    "# add lines to split the region\n",
    "fig.add_trace(go.Scatter(x=[100, 100], y=[-0.01, 0.15], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[500, 500], y=[-0.01, 0.15], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[4, 2000], y=[0.02, 0.02], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[4, 2000], y=[0.01, 0.01], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.update_traces(textposition='top center')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YMVTki9CUzUW"
   },
   "outputs": [],
   "source": [
    "pio.write_html(fig, file='index.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GacAyzmYlBHo"
   },
   "source": [
    "### 2020-03-02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1058,
     "status": "ok",
     "timestamp": 1583462609972,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "X21VlK93WnAj",
    "outputId": "735c97f9-ad96-4891-8261-c217a1a916e9"
   },
   "outputs": [
    {
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       "</body>\n",
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      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = datetime.date(2020, 3, 2)\n",
    "frm = country_ex_china.loc[(slice(None), date), :].reset_index()\n",
    "#frm = frm[frm['cum_confirmed'] >= 5]  # only show countries with more than 5 confirmed cases\n",
    "frm = frm[frm['color'] != 'Other']\n",
    "fig = px.scatter(frm,\n",
    "                 x='cum_confirmed', \n",
    "                 y='mortality_rate',  \n",
    "                 size='new_confirmed_logMA', \n",
    "                 size_max=frm['new_confirmed_logMA'].max() * 8,\n",
    "                 color='color',\n",
    "                 text='country/region',\n",
    "                 #color_continuous_scale=px.colors.sequential.Jet,\n",
    "                 hover_data=['country/region', 'cum_dead', 'cum_confirmed', 'new_confirmed', 'new_dead'],\n",
    "                 title='Mortality Rate vs Confirmed by Country/Region as of ' + str(date))\n",
    "fig.update_layout(xaxis_type=\"log\", xaxis_title='Confirmed Count', yaxis_title='Mortality Rate')\n",
    "fig.layout.yaxis.tickformat = ',.1%'  # set display to % format\n",
    "fig.update_traces(textposition='top center')\n",
    "# add lines to split the region\n",
    "fig.add_trace(go.Scatter(x=[100, 100], y=[-0.01, 0.08], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[500, 500], y=[-0.01, 0.08], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[20, 5000], y=[0.02, 0.02], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[20, 5000], y=[0.01, 0.01], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NxmpzPpgoG5A"
   },
   "source": [
    "### Comparison: China has been stable in the past two weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oXHO8VEpbeot"
   },
   "outputs": [],
   "source": [
    "china_frm = dxy_frm.copy()\n",
    "china_frm['province_name'] = np.where(china_frm['city_name'] == '武汉', '武汉', china_frm['province_name'])  # treat Wuhan as a province\n",
    "china_frm['province_name'] = np.where(china_frm['province_name'] == '湖北省', '湖北省除武汉', china_frm['province_name'])\n",
    "china_province = china_frm.groupby(['province_name', 'update_date']).agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 345,
     "status": "ok",
     "timestamp": 1583463681049,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
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     "user_tz": 360
    },
    "id": "kX4yFshIsrTj",
    "outputId": "117b6730-5501-448d-fede-45c05b79c3a2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:856: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:856: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in log10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "china_province = utils.add_moving_average(china_province, ['province_name'], 3)\n",
    "china_province['mortality_rate'] = china_province['cum_dead'] / china_province['cum_confirmed']\n",
    "china_province['new_confirmed_logMA'] = np.log10(china_province['new_confirmed_MA'] + 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
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    "colab_type": "code",
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     "status": "ok",
     "timestamp": 1583464937644,
     "user": {
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      "photoUrl": "",
      "userId": "02019284238930330026"
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    },
    "id": "LNFymX64suqo",
    "outputId": "3ae212c5-7f7c-46fc-ee9d-6afbbc98bc6d"
   },
   "outputs": [
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      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = datetime.date(2020, 2, 19)\n",
    "frm = china_province.loc[(slice(None), date), :].reset_index()\n",
    "#frm = frm[frm['cum_confirmed'] >= 5]  # only show countries with more than 5 confirmed cases\n",
    "#frm = frm[frm['color'] != 'Other']\n",
    "fig = px.scatter(frm,\n",
    "                 x='cum_confirmed', \n",
    "                 y='mortality_rate',  \n",
    "                 size='new_confirmed_logMA', \n",
    "                 size_max=frm['new_confirmed_logMA'].max() * 8,\n",
    "                 #color='color',\n",
    "                 text='province_name',\n",
    "                 #color_continuous_scale=px.colors.sequential.Jet,\n",
    "                 hover_data=['province_name', 'cum_dead', 'cum_confirmed', 'new_confirmed', 'new_dead'],\n",
    "                 title='Mortality Rate vs Confirmed by Country/Region as of ' + str(date))\n",
    "fig.update_layout(xaxis_type=\"log\", xaxis_title='Confirmed Count', yaxis_title='Mortality Rate')\n",
    "fig.layout.yaxis.tickformat = ',.1%'  # set display to % format\n",
    "# add lines to split the region\n",
    "fig.add_trace(go.Scatter(x=[100, 100], y=[-0.005, 0.05], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[500, 500], y=[-0.005, 0.05], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[15, 50000], y=[0.02, 0.02], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[15, 50000], y=[0.01, 0.01], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.update_traces(textposition='top center')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bzmiKebj24Zi"
   },
   "source": [
    "### China on 2020-03-02, bubbles are diminishing, so spread is slowed down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 795,
     "status": "ok",
     "timestamp": 1583464853082,
     "user": {
      "displayName": "jian Xu",
      "photoUrl": "",
      "userId": "02019284238930330026"
     },
     "user_tz": 360
    },
    "id": "UimRZgiEtUQ5",
    "outputId": "8431fcc0-468b-4c03-b29c-dd1212352517"
   },
   "outputs": [
    {
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      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = datetime.date(2020, 3, 2)\n",
    "frm = china_province.loc[(slice(None), date), :].reset_index()\n",
    "#frm = frm[frm['cum_confirmed'] >= 5]  # only show countries with more than 5 confirmed cases\n",
    "#frm = frm[frm['color'] != 'Other']\n",
    "fig = px.scatter(frm,\n",
    "                 x='cum_confirmed', \n",
    "                 y='mortality_rate',  \n",
    "                 size='new_confirmed_logMA', \n",
    "                 size_max=frm['new_confirmed_logMA'].max() * 8,\n",
    "                 #color='color',\n",
    "                 text='province_name',\n",
    "                 #color_continuous_scale=px.colors.sequential.Jet,\n",
    "                 hover_data=['province_name', 'cum_dead', 'cum_confirmed', 'new_confirmed', 'new_dead'],\n",
    "                 title='Mortality Rate vs Confirmed by Country/Region as of ' + str(date))\n",
    "fig.update_layout(xaxis_type=\"log\", xaxis_title='Confirmed Count', yaxis_title='Mortality Rate')\n",
    "fig.layout.yaxis.tickformat = ',.1%'  # set display to % format\n",
    "# add lines to split the region\n",
    "fig.add_trace(go.Scatter(x=[100, 100], y=[-0.005, 0.05], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[500, 500], y=[-0.005, 0.05], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[50, 50000], y=[0.02, 0.02], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.add_trace(go.Scatter(x=[50, 50000], y=[0.01, 0.01], mode=\"lines\", line={'dash': 'dot', 'color': 'orange'}))\n",
    "fig.update_traces(textposition='top center')\n",
    "\n",
    "fig.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xtTHiZCo28YT"
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   "outputs": [],
   "source": []
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